The invention relates generally to the field of image processing, and in particular to the field of image assessment and understanding.
Image assessment and understanding deal with problems that are easily solved by human beings given their intellectual faculties but are extremely difficult to solve by fully automated computer systems. Image understanding problems that are considered important in photographic applications include main subject detection, scene classification, sky and grass detection, people detection, automatic detection of orientation, etc. In a variety of applications that deal with a group of pictures, it is important to rank the images in terms of a logical order, so that they can be processed or treated according to their order. A photographic application of interest is automatic albuming, where a group of digital images are automatically organized into digital photo albums. This involves clustering the images into separate events and then laying out each event in some logical order, if possible. This order implies at least some attention to the relative content of the images, i.e., based on the belief that some images would likely be preferred over others.
A number of known algorithms, such as dud detection, event detection and page layout algorithms, are useful in connection with automatic albuming applications. Dud detection addresses the elimination, or de-emphasis, of duplicate images and poor quality images, while event detection involves the clustering of images into separate events by certain defined criteria, such as date and time. Given a set of images that belong to the same event, the objective of page layout is to layout each event in some logical and pleasing presentation, e.g., to find the most pleasing and space-efficient presentation of the images on each page. It would be desirable to be able to select the most important image in the group of images, e.g., the one that should receive the most attention in a page layout.
Due to the nature of the image assessment problem, i.e., that an automated system is expected to generate results that are representative of high-level cognitive human (understanding) processes, the design of an assessment system is a challenging task. Effort has been devoted to evaluating text and graphical data for its psychological effect, with the aim of creating or editing a document for a particular visual impression (see, e.g., U.S. Pat. Nos. 5,875,265 and 5,424,945). In the ""265 patent, a system analyzes an image, in some case with the aid of an operator, to determine correspondence of visual features to sensitive language that is displayed for use by the operator. The difficulty in this system is that the visual features are primarily based on low level features, i.e., color and texture, that are not necessarily related to image content, and a language description is difficult is to use for relative ranking of images. The ""945 patent discloses a system for evaluating the psychological effect of text and graphics in a document. The drawback with the ""945 patent is that it evaluates the overall visual impression of the document, without regard to its specific content, which reduces its usefulness for developing relative ranking. Besides their complexity and orientation toward discernment of a psychological effect, these systems focus on the analysis and creation of a perceptual impression rather than on the assessment and utilization of an existing image.
In digital imaging systems that process photographic images from a customer order, the same image processing path, or series of image processing steps, is usually applied to all the digital images related to that customer order. For example, in performing noise cleaning or interpolation on the digital signals obtained from such images, the same noise cleaning algorithm and the same interpolation algorithm is ordinarily applied to each of the photographic images from the order.
U.S. Pat. No. 5,694,484 (Cottrell et al.) describes a system involving several image processing modules and a method for selecting an image processing parameter that will optimize image quality for a given digital image, using information about the image capture device and the intended image output device. The method involves calculating an image quality metric that can be expressed as a series of mathematical transformations. The parameters used to control the image processing modules are varied, the image quality metric is calculated for each permutation of the control parameters, and the control parameters setting which yielded the best value of the image quality metric are used to process the digital image. The method described by Cottrell et al is performed on an individual digital image basis and therefore does not include an assessment of the quality of a digital image in either a relative or absolute basis relative to other digital images.
What is needed is an automatic digital imaging algorithm which can make an intelligent decision without user input as to which images within a set of images should be given preferential image processing treatment.
The present invention is directed to overcoming one or more of the problems set forth above. Briefly summarized, according to one aspect of the present invention, a method is disclosed for varying the image processing path for a digital image involving the steps of (a) computing an image processing attribute value for the digital image based on a determination of the degree of importance, interest or attractiveness of the image; and (b) using the image processing attribute value to control the image processing path for the image. In one embodiment, the image processing attribute value is based on an appeal value determined from the degree of importance, interest or attractiveness that is intrinsic to the image. In another embodiment, wherein the image is one of a group of digital images, the image processing attribute value is based on an emphasis value determined from the degree of importance, interest or attractiveness of the image relative to other images in the group of images.
The determination of the degree of importance, interest or attractiveness of an image is based on an assessment of the image with respect to certain features, wherein one or more quantities are computed that are related to one or more features in each digital image, including one or more features pertaining to the content of the individual digital image. The quantities are processed with a reasoning algorithm that is trained on the opinions of one or more human observers, and an output is obtained from the reasoning algorithm that assesses each image. In a dependent aspect of the invention, the features pertaining to the content of the digital image include at least one of people-related features and subject-related features. Moreover, additional quantities may be computed that relate to one or more objective measures of the digital image, such as colorfulness or sharpness. The results of the reasoning algorithm are processed to rank order the quality of each image in the set of images. The image processing modules applied to each digital image are varied based on the degree of importance, interest or attractiveness of the image, determined as by itself or as related to the group of digital images.
From another aspect, the invention may be seen (a) as a method for varying an image processing path for a digital image based on a determination of the appeal of the image with respect to certain self-salient features, wherein appeal is an assessment of the degree of importance, interest or attractiveness of an individual image or (b) as a method for varying an image processing path for a digital image based on a determination of the emphasis of an image with respect to certain features, wherein emphasis is an assessment of the degree of importance, interest or attractiveness of an individual image relative to other images in a group of images. From this perspective, for both appeal and emphasis assessment, self-salient image features are calculated, such as:
a. People related features: the presence or absence of people, the amount of skin or face area and the extent of close-up based on face size.
b. Objective features: the colorfulness and sharpness of the image.
c. Subject related features: the size of main subject and the goodness of composition based on main subject mapping.
While the above-noted features are adequate for emphasis assessment, it is preferable that certain additional relative-salient image features are considered for appeal assessment, such as:
a. The representative value of each image in terms of color content.
b. The uniqueness of the picture aspect format of each image.
An assessment of an image is obtained with a reasoning engine, such as a Bayesian network, which accepts as input the above-noted features and is trained to generate image assessment values. This assessment may be an intrinsic assessment for individual images, in which case the self-salient features are processed by a Bayesian network trained to generate the image appeal values, or the assessment may be a relative assessment for a group of images, in which case the self-salient and, optionally, the relative-salient features are processed by a Bayesian network trained to generate image emphasis values.
The advantage of the invention lies in its ability to perform an assessment of one or more images and accordingly vary the image processing path of a digital image without human intervention. In a variety of applications that deal with a group of pictures, such an algorithmic assessment enables the automatic control of image processing, so that the images can be more efficiently processed or treated according to their rank order.